Adaptive smart antenna processing may be used in a communication station (e.g., a base station) equipped with multiple antennas to either reject interference when communicating from a subscriber unit to the communication station (i.e., on the uplink) or to deliver power in a spatially or spatio-temporally selective manner when communicating from the communication station to a subscriber unit (i.e., on the downlink). Smart antenna communication systems may use linear spatial processing as part of the adaptive smart antenna processing of the received signals during uplink communications. In this situation, amplitude and phase adjustments are applied, typically in baseband, to each of the signals received at the antenna array elements to select (i.e., preferentially receive) the signals of interest while minimizing any signals or noise not of interest (i.e., interference). Such amplitude and phase adjustment can be described by a complex valued weight, the receive weight, and the receive weights for all elements of the array can be described by a complex valued vector, the receive weight vector.
Similarly, the downlink signal transmitted from the base station to remote receivers may be formed by adjusting, typically, but not necessarily in baseband, the amplitude and phase of the signals that are transmitted by each of the antennas of the antenna array. Such amplitude and phase control can be described by a complex valued weight, the transmit weight, and the weights for all elements of the array by a complex valued vector, the transmit weight vector. In some systems, the receive (and/or transmit) weights include temporal processing terms, and in such cases, the receive (and/or transmit) weights may be functions of frequency and applied in the frequency domain or, equivalently, functions of time applied in the form of convolution kernels. Alternatively, each convolution kernel may itself be described by a set of complex numbers, so that the vector of convolution kernels may be re-written as a complex valued weight vector, which, for the case of there being M antennas and each kernel having K entries, would be a vector of KM entries.
The RF environment in which the base station is operating (i.e., the number and location of users of the communication systems and sources of interference, the physical propagation environment, and the condition of the transmitting and receiving systems) may change dynamically during usage of the communication system. In such a situation, an adaptive smart antenna processing system can be used to modify the system's processing strategies (e.g., modify the weights applied) for forming the transmitted beam and processing the received signals to compensate for the changes. This provides a method of adapting the signal transmission and received signal processing operations to reflect changes in the operating environment.
One approach to modifying the signal transmission and received signal processing characteristics of a communication system uses the transmission of training signals which are previously known at the receiver. By examining the form of the known data when it is received, the receiver can estimate the user and interferer spatial or spatio-temporal signatures. This is useful because signature estimation facilitates the computation of downlink and uplink weights. The receive spatial signature characterizes how the base station array receives signals from a particular remote (e.g., subscriber) unit in the absence of any interference or other subscriber units. The transmit spatial signature of a particular user characterizes how that remote user receives signals from the base station in the absence of any interference. See U.S. Pat. No. 5,592,490, entitled “SPECTRALLY EFFICIENT HIGH CAPACITY WIRELESS COMMUNICATION SYSTEMS”, to Barratt, et al., and U.S. Pat. No. 5,828,658, entitled “SPECTRALLY EFFICIENT HIGH CAPACITY WIRELESS COMMUNICATION SYSTEMS WITH SPATIO-TEMPORAL PROCESSING”, to Ottersten, et al., both of which are assigned to the assignee of the present invention and the contents of which are incorporated herein by reference.
Another method of modifying the signal transmission and received signal processing characteristics of a communication system is based on inserting a period of silence at a know position in the transmitted signal. This is useful because all other signal energy received during this interval is assumed to constitute interference and thus represents a characteristic of the operating environment. See U.S. patent application Ser. No. 08/729,387, entitled “ADAPTIVE METHOD FOR CHANNEL ASSIGNMENT IN A CELLULAR COMMUNICATION SYSTEM”, to Yun, et al., assigned to the assignee of the present invention and the contents of which is incorporated herein by reference.
Thus, the training sequence and period of silence approaches both involve inserting data having a known characteristic into a set of data and then observing how that known data (or lack thereof) appears after propagation through the desired environment. The difference between the form of the known transmitted data and the received data is then used to estimate the characteristics of the environment and improve the processing of the unknown received data. While both of these approaches are useful, they have significant disadvantages because both methods expend valuable spectral bandwidth on signals which do not carry actual data. Furthermore, the methods are limited because they are only reliable to identify interference that overlaps spectrally or temporally with the training data or silence period.
So called “blind” methods are also known and used to improve the formation of transmitted signals and the processing of received signals. Such methods use one or more properties of the received signal, such as a geometric propagation model, cyclo-stationarity, constant envelope, or finite alphabet, to separate the desired user signal from interference. The resulting estimated signals and parameters for the properties are then used to formulate uplink and downlink signal processing strategies. While blind methods can respond robustly to changes in the interference environment, these methods suffer from a high computational burden, the need for a large amount of data over which the interference environment remains substantially constant, and the “association problem.” The latter problem entails deciding which of the communication waveforms present in the received data is the signal of interest and which are produced by interferers.
Blind weight determining methods usually are iterative. The speed of convergence of any iterative method is usually dependent on the quality of the initial value used for the weights in the iterative method. One common approach is to use a previously determined value of weights. In a rapidly changing interference environment, the quality of such a set of weights may be significantly degraded from when the set was determined.
Thus, there is a need in the art for apparatus and methods that overcome the limitations of known methods for processing transmitted or received signals to account for changes in the operating environment. In this regard, it is desirable to have such apparatus and methods which do not require training data. It is also desirable to have apparatus and methods with modest data computational requirements and which are capable of responding quickly and robustly to even large changes in the interference environment. It is also desirable to have methods that can rapidly compute a processing strategy that can be used as an initial condition for iterative strategy determination.